# General-Purpose Retrieval-Enhanced Medical Prediction Model Using Near-Infinite History

arXiv: https://arxiv.org/abs/2310.20204 · HTML: https://mlsanity.com/paper/2310.20204/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 40 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [Recurrent Memory Transformer](https://mlsanity.com/paper/2207.06881/) (2022)
- [Mega: Moving Average Equipped Gated Attention](https://mlsanity.com/paper/2209.10655/) (2022)
- [MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III](https://mlsanity.com/paper/1907.08322/) (2019)
- [Scaling Transformer to 1M tokens and beyond with RMT](https://mlsanity.com/paper/2304.11062/) (2023)
- [Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data](https://mlsanity.com/paper/2001.05295/) (2020)
- [GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning](https://mlsanity.com/paper/2207.09858/) (2022)
- [Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding](https://mlsanity.com/paper/2108.03625/) (2021)
- [Literature-Augmented Clinical Outcome Prediction](https://mlsanity.com/paper/2111.08374/) (2021)
- [UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge](https://mlsanity.com/paper/2211.08082/) (2022)
- [Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes](https://mlsanity.com/paper/2308.08494/) (2023)
- [Embedding Electronic Health Records for Clinical Information Retrieval](https://mlsanity.com/paper/1811.05402/) (2018)
- [All Data Inclusive, Deep Learning Models to Predict Critical Events in the Medical Information Mart for Intensive Care III Database (MIMIC III)](https://mlsanity.com/paper/2009.01366/) (2020)

## Built on by (most cited first, 12 of 12)

- [Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHRs](https://mlsanity.com/paper/2412.16178/) (2024)
- [A Collection of Innovations in Medical AI for patient records in 2024](https://mlsanity.com/paper/2503.05768/) (2025)
- [LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic Health Records](https://mlsanity.com/paper/2502.14259/) (2025)
- [EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records](https://mlsanity.com/paper/2506.04831/) (2025)
- [Time-Aware Attention for Enhanced Electronic Health Records Modeling](https://mlsanity.com/paper/2507.14847/) (2025)
- [Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records](https://mlsanity.com/paper/2409.07012/) (2024)
- [Multi-lingual Multi-institutional Electronic Health Record based Predictive Model](https://mlsanity.com/paper/2604.00027/) (2026)
- [LML-DAP: Language Model Learning a Dataset for Data-Augmented Prediction](https://mlsanity.com/paper/2409.18957/) (2024)
- [ReToP: Learning to Rewrite Electronic Health Records for Clinical Prediction](https://mlsanity.com/paper/2601.19286/) (2026)
- [EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records](https://mlsanity.com/paper/2605.12335/) (2026)
- [Learning to Select Source-Traceable Evidence for Language-Model Prediction from Irregular Clinical Time Series](https://mlsanity.com/paper/2605.20292/) (2026)
- [EviGen: Predictive Evidence Scaffolding for Verifiable Clinical Rationale Generation](https://mlsanity.com/paper/2609.18852/) (2026)

## Abstract (arXiv)

Machine learning (ML) has recently shown promising results in medical predictions using electronic health records (EHRs). However, since ML models typically have a limited capability in terms of input sizes, selecting specific medical events from EHRs for use as input is necessary. This selection process, often relying on expert opinion, can cause bottlenecks in development. We propose Retrieval-Enhanced Medical prediction model (REMed) to address such challenges. REMed can essentially evaluate unlimited medical events, select the relevant ones, and make predictions. This allows for an unrestricted input size, eliminating the need for manual event selection. We verified these properties through experiments involving 27 clinical prediction tasks across four independent cohorts, where REMed outperformed the baselines. Notably, we found that the preferences of REMed align closely with those of medical experts. We expect our approach to significantly expedite the development of EHR prediction models by minimizing clinicians' need for manual involvement.